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Rank Based Two Stage Semi-Supervised Deep Learning Model for X-Ray Images Classification

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Title Rank Based Two Stage Semi-Supervised Deep Learning Model for X-Ray Images Classification
 
Creator Mall, Pawan Kumar
Narayan, Vipul
Srivastava, Swapnita
Sabarwal, Munish
Kumar, Vimal
Awasthi, Shashank
Tyagi, Lalit
 
Subject Labeled dataset
RTS-SS-DL
Self-organising classifier
Semi-supervised learning
Shoulder’s fracture classification
 
Description 818-830
Deep learning approaches rely on a wide-scale labeled dataset to attain a high level of performance. Although labeled
data is more difficult and costly to access in some applications, such as bioinformatics and medical imaging, wide variety of
ongoing research on the topic of Semi-Supervised Deep Learning (SSDL) can improve and fix underlying problems in this
domain. The motivation for the suggested model Rank Based Two-Stage Semi-Supervised Deep Learning (RTS-SS-DL) is
the same as how doctors deal with unobserved or suspect cases in day to day practice. The physicians deal with these
suspect instances with the help of professional assistance from their colleagues. Before beginning therapy, some patients
seek the opinion of a variety of skilled professionals. The patients are treated by the most appropriate (vote count)
professional diagnosis. Our model (RTS-SS-DL) has achieved impressive metrics including 92.776% accuracy, 97.376%
specificity, 86.932% sensitivity, 96.192% precision, 85.644% MCC (Matthews Correlation Coefficient), 3.808% FDR
(False Discovery Rate), 2.624% FPR (False Positive Rate), 91.072% f1-score, 90.85% NPV (Negative Predictive Value),
and 13.068% FNR (False Negative Rate) for the unseen dataset. The outcome of this research results in an SSDL model that
is both more precise and effective.
 
Date 2023-08-09T04:37:41Z
2023-08-09T04:37:41Z
2023-08
 
Type Article
 
Identifier 0022-4456 (Print); 0975-1084 (Online)
http://nopr.niscpr.res.in/handle/123456789/62414
https://doi.org/10.56042/jsir.v82i08.3396
 
Language en
 
Publisher NIScPR-CSIR,India
 
Source JSIR Vol.82(08) [August 2023]